Prediction Of Academic Achievement Of Pamulang University Students Using Artificial Neural Networks

Abstract

This study aims to develop a prediction model for academic achievement of Pamulang University students using the artificial neural network (ANN) method. Student academic achievement is one of the important factors that reflect the quality of education in higher education. Therefore, accurate predictions can help universities take strategic steps, such as providing more appropriate academic support or designing coaching programs to improve student achievement. This study uses ANN because of its ability to identify complex patterns in data and provide accurate predictions based on the input provided. The data used in this study consisted of various variables, such as exam scores, number of absences, involvement in extracurricular activities, and other factors that influence student academic achievement. The artificial neural network model was trained using historical data of Pamulang University students and then tested to measure the accuracy of the predictions produced. The results of the study indicate that ANN can be used to predict student academic achievement with a high level of accuracy. The developed model shows good potential in helping universities monitor and improve the quality of education, as well as providing a clearer picture of the factors that influence student academic success. Thus, this study suggests the use of artificial neural network methods as a tool in academic information systems to support data-based decision making at Pamulang University, with the hope of improving the quality of learning and overall student achievement.